Julia Set Plotting Extension

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from juliaset import JuliaSet


    

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# Math libraries
import numpy as np
from math import sqrt

# Matplotlib plotting libraries
import matplotlib.pyplot as plt
%matplotlib inline

# Bokeh plotting libraries
import bokeh.plotting as blt
blt.output_notebook()


    

    





    
        
        
        
    

        
        BokehJS successfully loaded.
    

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class JuliaSetPlot(JuliaSet):
    """Extend JuliaSet to add plotting functionality"""
    
    def __init__(self, *args, **kwargs):
        # Invoke constructor for JuliaSet first, unaltered
        JuliaSet.__init__(self, *args, **kwargs)
        # Add another attribute: a rendered image array
        self.img = np.array([])
    
    def get_dim(self):
        """Return linear number of points in axis"""
        return int(4.0 / self._d)
    
    def render(self):
        """Render image as square array of ints"""
        if not self.set: self.generate()
        # Convert inefficient list to efficient numpy array
        self.img = np.array(self.set)
        # Reshape array into a 2d complex plane
        dim = int(sqrt(len(self.img)))
        self.img = np.reshape(self.img, (dim,dim)).T
        
    def show(self):
        """Use matplotlib to plot image as an efficient mesh"""
        if not self.img.size: self.render()
        plt.figure(1, figsize=(12,9))
        xy = np.linspace(-2,2,self.get_dim())
        plt.pcolormesh(xy, xy, self.img, cmap=plt.cm.hot)
        plt.colorbar()
        plt.show()
        
    def interact(self):
        """Use bokeh to plot an interactive image"""
        from matplotlib.colors import rgb2hex
        if not self.img.size: self.render()
        # Mimic matplotlib "hot" color palette
        colormap = plt.cm.get_cmap("hot")
        bokehpalette = [rgb2hex(m) for m in colormap(np.arange(colormap.N))]
        # Create bokeh figure
        f = blt.figure(x_range=(-2,2), y_range=(-2,2), plot_width=600, plot_height=600)
        f.image(image=[self.img], x=[-2], y=[-2], dw=[4], dh=[4], palette=bokehpalette, dilate=True)
        blt.show(f)


    

In [32]:

    

j = JuliaSetPlot(-1.037 + 0.17j)
%time j.set_spacing(0.006)
%time j.generate()
%time j.interact()


    

    




CPU times: user 348 ms, sys: 12 ms, total: 360 ms
Wall time: 379 ms
CPU times: user 3.04 s, sys: 8 ms, total: 3.05 s
Wall time: 4.77 s






    















    




CPU times: user 236 ms, sys: 44 ms, total: 280 ms
Wall time: 284 ms

In [33]:

    

j = JuliaSetPlot(-0.624 + 0.435j)
%time j.set_spacing(0.006)
%time j.generate()
%time j.interact()


    

    




CPU times: user 296 ms, sys: 4 ms, total: 300 ms
Wall time: 438 ms
CPU times: user 2.82 s, sys: 8 ms, total: 2.83 s
Wall time: 3.47 s






    















    




CPU times: user 304 ms, sys: 64 ms, total: 368 ms
Wall time: 995 ms

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%prun j.generate()


    

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%load_ext line_profiler
%lprun -f j.generate j.generate()


    

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%time


    

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